Deep Learning of Sparse Patterns in Medical IoT for Efficient Big Data Harnessing
The long-term and continuous streaming of big data from medical Internet of Things (IoT), poses a great challenge for the battery-limited tiny devices. To address this challenge, we propose a novel framework for medical IoT data sparsification, leveraging both deep learning and optimal space searchi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10068236/ |
| _version_ | 1827986615718379520 |
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| author | Junhua Wong Qingxue Zhang |
| author_facet | Junhua Wong Qingxue Zhang |
| author_sort | Junhua Wong |
| collection | DOAJ |
| description | The long-term and continuous streaming of big data from medical Internet of Things (IoT), poses a great challenge for the battery-limited tiny devices. To address this challenge, we propose a novel framework for medical IoT data sparsification, leveraging both deep learning and optimal space searching. More specifically, the deep sparsification networks are designed to learn to extract key sparse patterns in the medical IoT data, by projecting the original data stream to a sparsified data representation. Further, the principles for designing deep encoding networks have been analyzed by an optimal space searching strategy, aiming to determine the best deep sparsification architecture that meets the energy constraint or sparsification error constraint. Compared with state-of-the-art approaches, our deep learning-based and space search-optimized framework shows a dramatic capability to tackle the power hungriness problem on medical IoT big data. This novel study, by enabling energy-efficient medical IoT big data sparsification, is expected to boost the continuous and long-term medical IoT applications, such as cardiac monitoring, thereby advancing precision medicine. |
| first_indexed | 2024-04-09T23:34:17Z |
| format | Article |
| id | doaj.art-a08e84006d0b4680a3b16aa8ad57fe19 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T23:34:17Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a08e84006d0b4680a3b16aa8ad57fe192023-03-20T23:00:24ZengIEEEIEEE Access2169-35362023-01-0111258562586410.1109/ACCESS.2023.325672110068236Deep Learning of Sparse Patterns in Medical IoT for Efficient Big Data HarnessingJunhua Wong0Qingxue Zhang1https://orcid.org/0000-0001-7125-7928School of Engineering and Technology, Purdue University, Indinapolis, IN, USASchool of Engineering and Technology, Purdue University, Indinapolis, IN, USAThe long-term and continuous streaming of big data from medical Internet of Things (IoT), poses a great challenge for the battery-limited tiny devices. To address this challenge, we propose a novel framework for medical IoT data sparsification, leveraging both deep learning and optimal space searching. More specifically, the deep sparsification networks are designed to learn to extract key sparse patterns in the medical IoT data, by projecting the original data stream to a sparsified data representation. Further, the principles for designing deep encoding networks have been analyzed by an optimal space searching strategy, aiming to determine the best deep sparsification architecture that meets the energy constraint or sparsification error constraint. Compared with state-of-the-art approaches, our deep learning-based and space search-optimized framework shows a dramatic capability to tackle the power hungriness problem on medical IoT big data. This novel study, by enabling energy-efficient medical IoT big data sparsification, is expected to boost the continuous and long-term medical IoT applications, such as cardiac monitoring, thereby advancing precision medicine.https://ieeexplore.ieee.org/document/10068236/Big datadeep learningIoT big dataspace search |
| spellingShingle | Junhua Wong Qingxue Zhang Deep Learning of Sparse Patterns in Medical IoT for Efficient Big Data Harnessing IEEE Access Big data deep learning IoT big data space search |
| title | Deep Learning of Sparse Patterns in Medical IoT for Efficient Big Data Harnessing |
| title_full | Deep Learning of Sparse Patterns in Medical IoT for Efficient Big Data Harnessing |
| title_fullStr | Deep Learning of Sparse Patterns in Medical IoT for Efficient Big Data Harnessing |
| title_full_unstemmed | Deep Learning of Sparse Patterns in Medical IoT for Efficient Big Data Harnessing |
| title_short | Deep Learning of Sparse Patterns in Medical IoT for Efficient Big Data Harnessing |
| title_sort | deep learning of sparse patterns in medical iot for efficient big data harnessing |
| topic | Big data deep learning IoT big data space search |
| url | https://ieeexplore.ieee.org/document/10068236/ |
| work_keys_str_mv | AT junhuawong deeplearningofsparsepatternsinmedicaliotforefficientbigdataharnessing AT qingxuezhang deeplearningofsparsepatternsinmedicaliotforefficientbigdataharnessing |